Could self-reported physical performance help predict individuals at the highest risk of mortality and hospital admission events in clinical practice? Findings from the Hertfordshire Cohort Study

Aim: To consider how self-reported physical function measures relate to adverse clinical outcomes measured over 20 years of follow-up in a community-dwelling cohort (aged 59–73 at baseline) as compared with hand grip strength, a well-validated predictor of adverse events. Background: Recent evidence has emphasized the significant association of physical activity, physical performance, and muscle strength with hospital admissions in older people. However, physical performance tests require staff availability, training, specialized equipment, and space to perform them, often not feasible or realistic in the context of a busy clinical setting. Methods: In total, 2997 men and women were analyzed. Baseline predictors were measured grip strength (Jamar dynamometer) and the following self-reported measures: physical activity (Dallosso questionnaire); physical function score (SF-36 Health Survey); and walking speed. Participants were followed up from baseline (1998–2004) until December 2018 using UK Hospital Episode Statistics and mortality data, which report clinical outcomes using ICD-10 coding. Predictors in relation to the risk of mortality and hospital admission events were examined using Cox regression with and without adjustment for sociodemographic and lifestyle characteristics. Findings: The mean age at baseline was 65.7 and 66.6 years among men and women, respectively. Over follow-up, 36% of men and 26% of women died, while 93% of men and 92% of women were admitted to hospital at least once. Physical activity, grip strength, SF-36 physical function, and walking speed were all strongly associated with adverse health outcomes in both sex- and fully adjusted analyses; poorer values for each of the predictors were related to greater risk of mortality (all-cause, cardiovascular-related) and any, neurological, cardiovascular, respiratory, any fracture, and falls admissions. SF-36 physical function and grip strength were similarly associated with the adverse health outcomes considered.


Introduction
Aging is associated with a natural decline in physical function.This natural decline is associated with a range of public health issues including an increased level of dependency, premature mortality, risk of falls, and healthcare use (Langhammer et al., 2018).With the demographic shift toward an aging population, there is a growing need to address the health and well-being of older people, particularly their susceptibility to hospital admissions (Simmonds et al., 2014a, Simmonds et al., 2014b).Hospital admissions are not only a significant burden to healthcare systems but also have profound implications for individuals, impacting their quality of life, functional independence, and overall mortality risk.Like many other developed nations, the health system of the UK faces considerable challenges in meeting the needs of older individuals, particularly in the context of hospital admissions.
Recent evidence has emphasized the significant association of physical activity, physical performance, and muscle strength with hospital admissions in older people.Low levels of physical activity in middle age are associated with increased later hospital admission rates, highlighting the potential benefits of regular exercise for people in their midlife (Simmonds et al., 2014a, Luben et al., 2020).Impaired physical performance, such as reduced mobility and balance, has been linked to higher hospital admission risks, suggesting the importance of maintaining functional ability in middle and older ages to prevent health deterioration (Studenski et al., 2003).Moreover, declining muscle strength has been identified as a critical factor contributing to disability among older people, with emerging evidence highlighting the role of higher muscle strength in midlife contributing to overall well-being and disease prevention in later life (Soysal et al., 2021).
However, functional performance and muscle strength tests require staff availability, training, specialized equipment, and space to perform them, often not feasible or realistic in the context of a busy clinical setting.For example, grip strength assessment requires staff training in the procedure, a calibrated dynamometer, a specific chair, and the time to explain the procedure to a patient who is required to complete the task 3 times.Realistically this takes around 10 minutes.If a simpler, quicker, and more feasible screening tool was available, this might help clinicians target resources to those individuals at the highest risk.
To our knowledge, however, no studies have compared selfreported measures of physical function with objective measures regarding their association with adverse health events over 20 years of follow-up among community-dwelling older people.Therefore, in the current study, we considered how self-reported measures (Dallosso physical activity score, SF-36 physical function score, self-reported walking speed) related to adverse clinical outcomes measured over 20 years of follow-up in a community-dwelling cohort aged 59-73 years at baseline, as compared with hand grip strength, a well-validated predictor of adverse events.

The Hertfordshire Cohort Study
The Hertfordshire Cohort Study (HCS) consists of 2997 women and men born in Hertfordshire from 1931 to 1939 and who still lived there in 1998-2004 when they completed a home interview and clinic visit for a detailed health assessment.The HCS received ethical approval from the Hertfordshire and Bedfordshire Local Research Ethics Committee and all participants provided informed consent for the investigations they underwent in 1998-2004 and for researchers to access their medical records in the future.Further details of HCS have been previously published (Syddall et al., 2005, Syddall et al., 2019).
Ascertainment of participant information at baseline (1998)(1999)(2000)(2001)(2002)(2003)(2004) Information on smoking, alcohol consumption, and physical activity (Dallosso questionnaire) was ascertained by a nurseadministered questionnaire (Dallosso et al., 1988).Occupational social class was ascertained from most recent or current full-time occupation for men and among women who never married, and from husband's occupation for ever-married women.Occupations were then classified according to the 1990 Office of Population Censuses and Surveys (OPCS) Standard Occupational Classification (SOC90) unit group for occupation (Office of Population Censuses and Surveys, 1990).Self-reported physical function was assessed according to 10 questions from the physical functioning scale of the SF-36 Health Survey (Ware et al., 1993).Participants reported whether the following 10 activities were limited a little, limited a lot, or not limited at all due to their health: vigorous activities; moderate activities; lifting/carrying groceries; climbing several flights of stairs; climbing one flight of stairs; bending/kneeling; walking at least one mile; walking at least half a mile; walking 100 yards; and bathing/dressing.The total physical function score could range from 0 to 100 with higher scores indicating higher self-reported physical function.Self-reported walking speed was ascertained by asking participants which of the following responses best describes their walking speed: 'unable to walk'; 'very slow'; 'stroll at an easy pace'; 'normal speed'; 'fairly brisk'; or 'fast'.
At the baseline clinic, measurements were made of height (Harpenden pocket stadiometer, Chasmors Ltd, London, UK) and weight (SECA floor scale, Chasmors Ltd, London, UK) and these were used to derive body mass index (BMI).Grip strength was measured using a Jamar dynamometer according to a standardized protocol (Roberts et al., 2011).In brief, the participant was seated with their forearms resting on the chair arms and their wrist just over the end of the chair arm, in a neutral position with the thumbs facing upwards.They were asked to squeeze as hard as they could for as long as they could until the researcher told them to stop.Three trials on each hand were performed with alternating sides and the maximum absolute grip strength value from all six trials was used for analysis.

Ascertainment of adverse health-related events
Adverse health events were identified using mortality and Hospital Episode Statistics (HES) data.Permission to obtain a HES extract for HCS participants from 01/04/1998 to 31/12/2018 was granted by the Ethics and Confidentiality Committee of the National Information Governance Board and NHS Digital.Linkage of the HCS cohort with HES data has been previously described (Simmonds et al., 2014b); the HES extract included admission information such as the admission date, diagnoses coded to ICD-10, and date of discharge.Adverse health outcomes relating to admissions and deaths that occurred from the HCS baseline clinic (1998)(1999)(2000)(2001)(2002)(2003)(2004) until 31st December 2018 were identified using the ICD-10 codes as stated in Supplementary Table 1.

Statistical methods
Baseline participant characteristics and the proportion of participants who experienced various adverse health events during follow-up were described using summary statistics.Measures of physical activity, strength, and performance (Dallosso physical activity score, grip strength, SF-36 physical function score, and self-reported walking speed) were examined in relation to risk of adverse health outcomes using time-to-first event Cox regression.Sex-adjusted models and fully adjusted models, accounting for sex, age, height, BMI, smoking status (ever versus never), alcohol consumption, and occupational social class, were implemented; models for grip strength, SF-36 physical function, and selfreported walking speed were also adjusted for physical activity.Adverse health outcomes considered included those specified in Supplementary Table 1.For sensitivity analyses, competing risk analyses for the hospital-related events were performed using the Fine-Gray subdistribution hazards model with death as a competing event (Fine and Gray, 1999).
Associations explored using Cox regression and competing risk analysis were examined among the pooled sample of men and women with adjustment for sex as associations were similar between men and women in sex-stratified analysis (data not shown).Standard deviation scores (z-scores) were derived for physical activity, grip strength, and SF-36 physical function and used in analyses.Analyses were performed using Stata, release 17.0; P < 0.05 was regarded as statistically significant.

Descriptive statistics
Summary statistics for the participant characteristics at baseline and adverse health events during follow-up are presented in Table 1.Mean age at baseline was 65.7 and 66.6 years among men and women, respectively.Mean physical activity scores were similar among men (60.9) and women (59.0), whereas mean grip strength was considerably higher among men (44.0 kg versus 26.5 kg).Median SF-36 physical function scores were higher among men (90) than women (85).Most participants (71% of men and 73% of women) rated their walking speed as normal or higher.
During follow-up, 36% of men and 26% of women died, while 93% of men and 92% of women experienced at least one hospital admission. Physical

Sensitivity analyses
Results from competing risk analyses are presented in Supplementary Table 2. Some associations were attenuated compared to when death was treated as a censoring event in time-to-first event Cox regression models.However, all four predictors were related to greater risk of the following types of admission in both Cox regression analyses (Table 2) and competing risk analyses (Supplementary Table 2): any, cardiovascular, respiratory, and falls.This was the case in both sex-and fully adjusted analyses.

Discussion
This study has demonstrated associations between self-reported measures of physical function when measured at a single time point among older people and adverse outcomes over 20 years of follow-up.For self-reported physical function as a domain of SF-36, associations were of a similar strength compared to using grip strength as a predictor; for example, both lower grip strength and poorer SF-36 physical function were associated with increased risk of mortality (all-cause, cardiovascular, and other) and the any, neurological, cardiovascular, respiratory, any fracture, and fallsrelated admissions.This is a significant finding, given that grip strength is a well-validated predictor of long-term clinical sequelae (Cheung et al., 2013, Kim et al., 2019).We did consider other associates of adverse clinical outcomes.For example, the Dallosso physical activity score, as a measure of physical activity levels, demonstrated a strong relationship with the adverse health outcomes considered.Lower scores were consistently associated with an increased risk of hospitalization.This aligns with previous research emphasizing the positive impact of regular physical activity on health outcomes, including the prevention of chronic conditions, improvement in cardiovascular health, and maintenance of functional independence (Simmonds et al., 2014a, Syddall et al., 2016).Our findings emphasize the need to promote and support physical activity programs tailored to older individuals to reduce the likelihood of hospital admissions.
Similarly, self-reported walking speed demonstrated an association with hospital admissions among older people.Slower walking speed was consistently correlated with a higher likelihood of hospitalization.Slower walking speed reflects reduced mobility, impaired physical function, and an increased vulnerability to falls and other adverse health events and can be self-reported or objectively recorded.A systematic review conducted using data from nine longitudinal cohorts in community-dwelling older populations showed that higher measured gait speed of older adults was associated with significant 5-year and 10-year survival, reinforcing its importance (Studenski et al., 2011).In a previous analysis comprising Hertfordshire Cohort Study participants, the relationship between self-reported and measured walking speed and their associations with clinical characteristics and mortality was examined (Syddall et al., 2015).Self-reported walking speed was strongly associated with measured walking speed among men and women (P < 0.001) and both measures were similarly associated with clinical characteristics and mortality.This suggests that self-reported walking speed could serve as a useful marker of physical performance in settings where direct measurement of walking speed is not feasible.
Overall, however, the SF-36 physical function score was more consistently associated with adverse health outcomes than physical activity and performed similarly to grip strength regarding its strength of association with these outcomes.As a self-reported questionnaire, the SF-36 Physical Function Survey allows for a relatively quick and efficient assessment of physical function without the need for specialized equipment or extensive training.Furthermore, the SF-36 Physical Function Survey captures various aspects of physical function, including limitations in activities of daily living, mobility, and overall functional independence.This comprehensive assessment provides a holistic understanding of an individual's physical capabilities and limitations.By identifying Primary Health Care Research & Development areas of impairment, healthcare professionals can address specific functional deficits and develop targeted interventions to mitigate the risk of hospital admissions.
It may be argued that the measurement of grip strength is worthwhile even though the SF-36 physical function score performed similarly to grip strength regarding its strength of association with adverse health outcomes.For example, grip strength is an objective measure and is important for the identification of sarcopenia according to the 2019 European Working Group on Sarcopenia in Older People (Cruz-Jentoft et al., 2019) and the Sarcopenia  Health outcomes not referring to deaths all correspond to hospital admission events.Time-to-first event Cox regression was used; death was regarded as a censoring event for hospital admission outcomes.Fully adjusted models accounted for sex, age, height, BMI, smoking status (ever versus never), alcohol consumption, and occupational social class; models for grip strength, SF-36 physical function, and self-reported walking speed were also adjusted for physical activity.Other causes of death were those that were not from cancer or cardiovascular causes.Standard deviation scores (z-scores) were derived for physical activity, grip strength, and SF-36 physical function; estimates are shown per SD reduction in these predictors.Statistically significant associations (P < 0.05) are highlighted in bold.
Primary Health Care Research & Development activity, strength, and function in relation to risk of adverse health outcomes Associations between physical activity, grip strength, SF-36 physical function, and self-reported walking speed in relation to adverse health outcomes are presented in Table2.All four predictors were strongly associated with many of the adverse health outcomes.In both sex-and fully adjusted analyses, poorer values for each of the predictors were related to greater risk of mortality (all-cause, cardiovascular, and other) and the following types of admission: any, neurological, cardiovascular, respiratory, any fracture, and falls.For example, fully adjusted hazard ratios for cardiovascular admissions per standard deviation reduction in physical activity, grip strength, and SF-36 physical function were 1.10 (1.05, 1.15), 1.19 (1.10, 1.29) and1.31(1.26,1.37), respectively; the corresponding hazard ratio per lower band of self-reported walking speed was 1.18 (1.12, 1.24).

Table 1 .
Baseline participant characteristics and adverse health events during follow-up

Table 2 .
Hazard ratios (95% CI) for physical activity, strength, and function measures in relation to adverse health outcomes